
!pip install jovian opendatasets --upgrade --quiet


!pip install plotly

Collecting plotly
  Downloading plotly-5.15.0-py2.py3-none-any.whl (15.5 MB)
     |████████████████████████████████| 15.5 MB 20.1 MB/s            
Requirement already satisfied: packaging in /opt/conda/lib/python3.9/site-packages (from plotly) (21.2)
Collecting tenacity>=6.2.0
  Downloading tenacity-8.2.2-py3-none-any.whl (24 kB)
Requirement already satisfied: pyparsing<3,>=2.0.2 in /opt/conda/lib/python3.9/site-packages (from packaging->plotly) (2.4.7)
Installing collected packages: tenacity, plotly
Successfully installed plotly-5.15.0 tenacity-8.2.2


import os


import pandas as pd


import numpy as np


from urllib.request import urlretrieve


project_name = "worldcup-course-project-meetth-new" # change this (use lowercase letters and hyphens only)


!pip install jovian --upgrade -q


urlretrieve('https://raw.githubusercontent.com/meetth77/jovian_practice/main/data_wcmatches','./new.csv')

('./new.csv', <http.client.HTTPMessage at 0x7f06bf295e20>)


#checking if new file is created or not
os.listdir('./')

['.git',
 '.jovianrc',
 'zerotopandas-course-project-Copy5.ipynb',
 '.ipynb_checkpoints',
 'new.csv']


#Importing data 
data_df=pd.read_csv('./new.csv')


#Looking at data and getting to know different columns and type of data in the columns also number of rows and columns in data
data_df


#Removing unwanted columns for data 
data_df.pop('Unnamed: 20')
data_df.pop('Unnamed: 21')

0      NaN
1      NaN
2      NaN
3      NaN
4      NaN
      ... 
599    NaN
600    NaN
601    NaN
602    NaN
603    NaN
Name: Unnamed: 21, Length: 604, dtype: object


#Getting columns list of data in order to arrange data in specific manner
data_df.columns

Index(['home_team', 'away_team', 'home_score', 'home_xg', 'home_penalty',
       'away_score', 'away_xg', 'away_penalty', 'home_manager', 'home_captain',
       'away_manager', 'away_captain', 'Attendance', 'Venue', 'Round', 'Date',
       'Score', 'Notes', 'Host', 'Year'],
      dtype='object')


#Arranging the data from data_df in specific format for easier understanding
matches_df=data_df[[
    'Year',
    'Host',
    'Date',
    'Round',
    'home_team',
    'away_team',
    'home_penalty',
    'away_penalty',
    'home_score',
    'away_score',
    'Score',
    'Notes',
    'Attendance',
    'Venue',
    'home_xg',
    'away_xg',
    'home_manager',
    'away_manager',
    'home_captain',
    'away_captain',
        ]]


#Replacing west germany with germany 
matches_df=matches_df.replace(['West Germany'],'Germany')


#checking the west germany is present in data or not 
matches_df[matches_df.home_team=='West Germany']


# Count of Empty values or null values in dataset
matches_df.isna().sum()

Year              0
Host              0
Date              0
Round             0
home_team         0
away_team         0
home_penalty    570
away_penalty    570
home_score        0
away_score        0
Score             0
Notes           550
Attendance        0
Venue             0
home_xg         476
away_xg         476
home_manager      0
away_manager      0
home_captain    320
away_captain    320
dtype: int64


#getting info related to new data frame like null values, data type of columns
matches_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 604 entries, 0 to 603
Data columns (total 20 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   Year          604 non-null    int64  
 1   Host          604 non-null    object 
 2   Date          604 non-null    object 
 3   Round         604 non-null    object 
 4   home_team     604 non-null    object 
 5   away_team     604 non-null    object 
 6   home_penalty  34 non-null     float64
 7   away_penalty  34 non-null     float64
 8   home_score    604 non-null    int64  
 9   away_score    604 non-null    int64  
 10  Score         604 non-null    object 
 11  Notes         54 non-null     object 
 12  Attendance    604 non-null    int64  
 13  Venue         604 non-null    object 
 14  home_xg       128 non-null    float64
 15  away_xg       128 non-null    float64
 16  home_manager  604 non-null    object 
 17  away_manager  604 non-null    object 
 18  home_captain  284 non-null    object 
 19  away_captain  284 non-null    object 
dtypes: float64(4), int64(4), object(12)
memory usage: 94.5+ KB


# Cleaned and arranged data is written into new csv file 
matches_df.to_csv('./wc_cleaned.csv')


import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
%matplotlib inline

sns.set_style('darkgrid')
matplotlib.rcParams['font.size'] = 14
matplotlib.rcParams['figure.figsize'] = (9, 5)
matplotlib.rcParams['figure.facecolor'] = '#00000000'


#No of none null values in Notes column
matches_df.Notes.notna().sum()

54


#No. of unique values in Notes column
len(pd.unique(matches_df.Notes))

24


#Getting unique no of team or participants in data
teams_df=matches_df.home_team.to_list()+matches_df.away_team.to_list()
teams_df=pd.DataFrame(teams_df)


# Number of uniques team participated in world cup since 1986 
teams=[]
teams=matches_df.home_team.to_list()+matches_df.away_team.to_list()
teams=set(teams)
teams=list(teams)


'Total Number of matches played in world cup from the year 1986 to 2022 are: {}.'.format(matches_df.home_team.count())

'Total Number of matches played in world cup from the year 1986 to 2022 are: 604.'


'Number of teams/countries participated in world cup from the year 1986 to 2022 are: {}.'.format(len(teams))

'Number of teams/countries participated in world cup from the year 1986 to 2022 are: 77.'


year_wise_df=matches_df.groupby('Year')


year_wise_df.sum()


#Getting years from data
years=pd.unique(matches_df.Year)
years

array([2022, 2018, 2014, 2010, 2006, 2002, 1998, 1994, 1990, 1986])


#getting no of teams participating in each year
team_years=[]
for i in years:
    team_years_home=matches_df[matches_df.Year==i].home_team
    team_years_away=matches_df[matches_df.Year==i].away_team
    team_years_append=team_years_home.append(team_years_away)
    team_years.append(len(pd.unique(team_years_append)))
team_years=team_years[::-1]
team_years

[24, 24, 24, 32, 32, 32, 32, 32, 32, 32]


#Getting unique list of host countries
host_countries=pd.unique(matches_df.Host)
host_countries

array(['Qatar', 'Russia', 'Brazil', 'South Africa', 'Germany',
       'Korea Republic, Japan', 'France', 'United States', 'Italy',
       'Mexico'], dtype=object)


#grouping the data based on years and making a new dataframe to store of year wise statistics
#Some year wise statistics
year_wise_data=pd.DataFrame()
year_wise_data['No_of_matches']=year_wise_df.count().Attendance
year_wise_data['No_of_teams']=team_years
year_wise_data['Attendance']=year_wise_df.sum().Attendance
year_wise_data['Average_attendance']=year_wise_data['Attendance']/year_wise_data['No_of_matches']
year_wise_data['Total_home_goals']=year_wise_df.sum().home_score
year_wise_data['Total_away_goals']=year_wise_df.sum().away_score
year_wise_data['Total_home_penalty']=year_wise_df.sum().home_penalty
year_wise_data['Total_away_penalty']=year_wise_df.sum().away_penalty

year_wise_data['Host']=host_countries[::-1]

ywd=year_wise_data
ywd['normal_goals']=ywd.Total_home_goals+ywd.Total_away_goals
ywd['Total_penalty']=ywd.Total_home_penalty+ywd.Total_away_penalty
ywd['Total_goals']=ywd['Total_penalty']+ywd['normal_goals']
ywd['Years']=ywd.index
ywd

ywd


#plotting the graph of attendance in world cup year wise in order to understand the trend
matplotlib.rcParams['font.size'] = 13
plt.rcParams['figure.figsize'] = (20,5)
plt.plot(ywd.index,ywd.Attendance,'.-b')
plt.xlabel('Years')
plt.ylabel('Attendance')
plt.title('World Cup Attendance')

Text(0.5, 1.0, 'World Cup Attendance')


#plotting the graph of attendance per match or average attendance in world cup year wise in order to understand the trend
plt.plot(ywd.index,ywd.Average_attendance,'.-r')
plt.xlabel('Year')
plt.ylabel('Average Attendance')
plt.title('World Cup Average Attendance')

Text(0.5, 1.0, 'World Cup Average Attendance')


#plotting the graph of attendance per match or average attendance in world cup host wise in order to understand the trend
plt.plot(ywd.Host,ywd.Attendance,'o-c')
plt.title('Attendance')
plt.ylabel("Attendance (in millions)")
plt.xlabel('Host Nation')

Text(0.5, 0, 'Host Nation')


#plotting the graph of attendance per match or average attendance in world cup host wise in order to understand the trend
plt.plot(ywd.Host,ywd.Average_attendance,'s-m')
plt.title('Attendance per match')
plt.ylabel("Average Attendance")
plt.xlabel('Host Nation')

Text(0.5, 0, 'Host Nation')


#adding years(List) in dataframe in order to make manipulate data according to our need. 
Years=years[::-1]
ywd['Years']=Years
ywd


#plotting the bar graph of home goals and away goals in world cup year wise in order to identify trends
#Using subplot and plotting two graphs side by side for comparison
fig,axes=plt.subplots(1,2)
sns.barplot(data=ywd,y='Years',x='Total_home_goals',orient='h',palette='flare',saturation=10,ax=axes[0])
axes[0].set_title('Home Goals by years')
axes[0].set_xlabel('Home Goals')

sns.barplot(data=ywd,y='Years',x='Total_away_goals',orient='h',palette='crest',saturation=10,ax=axes[1])
axes[1].set_title('Away Goals by years')
axes[1].set_xlabel('Away Goals')

Text(0.5, 0, 'Away Goals')


#plotting the line graph of home goals and away goals in same graph to compare home goals and away goals
matplotlib.rcParams['figure.figsize'] = (20, 5)
sns.lineplot(data=ywd,x='Years',y='Total_home_goals',marker='o',color='crimson')
sns.lineplot(data=ywd,x='Years',y='Total_away_goals',marker='s',color='darkblue')
plt.title('Goals scored')
plt.xlabel('Year')
plt.ylabel('No. of goals scored')
plt.legend(['Home goals','Away goals'])

<matplotlib.legend.Legend at 0x7f06b31d6a90>


#plotting the line graph of home penalty goals and away penalty goals host wise in same graph to compare the same
sns.lineplot(data=ywd,x=ywd.Host,y='Total_home_goals',marker='s',color='crimson')
sns.lineplot(data=ywd,x='Host',y='Total_away_goals',marker='o',color='indigo')
plt.title('Goal Scored(1986-2022)')
plt.xlabel('Host Nation')
plt.ylabel('No. of Penalty')
plt.legend(['Home goals','Away goals'])

<matplotlib.legend.Legend at 0x7f06b31d6f70>


#plotting the line graph of home penalty goals and away penalty goals year wise in same graph to compare the same
matplotlib.rcParams['figure.figsize'] = (20, 5)
matplotlib.rcParams['font.size'] = 13
sns.lineplot(data=ywd,x='Years',y='Total_home_penalty',marker='s',color='slateblue')
sns.lineplot(data=ywd,x='Years',y='Total_away_penalty',marker='o',color='turquoise')
plt.title('Total Penalty')
plt.xlabel('Year')
plt.ylabel('No. of Penalty')
plt.legend(['Home penalty','Away penalty'])

<matplotlib.legend.Legend at 0x7f06b30cb400>


#plotting the line graph of home penalty goals and away penalty goals host wise in same graph to compare the same
sns.lineplot(data=ywd,x=ywd.Host,y='Total_home_penalty',marker='s',color='darkorange')
sns.lineplot(data=ywd,x='Host',y='Total_away_penalty',marker='o',color='indigo')
plt.title('Total Penalty (1986-2022)')
plt.xlabel('Host Nation')
plt.ylabel('No. of Penalty')
plt.legend(['Home penalty','Away penalty'])

<matplotlib.legend.Legend at 0x7f06b3005fd0>


matches_df


# Getting dataframe of when home team scored more goals
home_wins_df=matches_df[matches_df.home_score>matches_df.away_score][['Year','Host','Date','Venue','Round',
                                                                      'home_team','home_score','away_score',
                                                                      'Notes','home_manager','home_captain',]]


# Getting dataframe of when away team scored more goals
away_wins_df=matches_df[matches_df.home_score<matches_df.away_score][['Year','Host','Date','Venue','Round',
                                                                      'away_team','home_score','away_score',
                                                                      'Notes','away_manager','away_captain',]]


# Getting dataframe of Draws 
draws_df=matches_df[matches_df.home_score==matches_df.away_score][['Year','Host','Date','Venue','Round',
                                                                   'home_team','away_team','home_penalty','away_penalty',
                                                                   'Notes','home_manager','home_captain',
                                                                   'away_manager','away_captain',]]


#Reviewing data in home_win dataframe 
home_wins_df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 267 entries, 1 to 599
Data columns (total 11 columns):
 #   Column        Non-Null Count  Dtype 
---  ------        --------------  ----- 
 0   Year          267 non-null    int64 
 1   Host          267 non-null    object
 2   Date          267 non-null    object
 3   Venue         267 non-null    object
 4   Round         267 non-null    object
 5   home_team     267 non-null    object
 6   home_score    267 non-null    int64 
 7   away_score    267 non-null    int64 
 8   Notes         12 non-null     object
 9   home_manager  267 non-null    object
 10  home_captain  129 non-null    object
dtypes: int64(3), object(8)
memory usage: 25.0+ KB


#getting data of teams with most wins when playing as home team 
home_wins_df.home_team.value_counts().head(20)

Brazil            29
Germany           26
Argentina         26
France            17
Italy             15
Netherlands       14
Belgium           13
England           12
Portugal          11
Spain              9
Sweden             6
Korea Republic     6
Mexico             5
Uruguay            5
Colombia           4
Russia             4
Nigeria            4
Croatia            4
Cameroon           4
Switzerland        3
Name: home_team, dtype: int64


#Storing the data in new dataframe in order to visualize it 
mhw=home_wins_df.home_team.value_counts().head(15)
mhw_df=pd.DataFrame()
mhw_df['Teams']=mhw.index
mhwlist=[]
for i in range(0,15):
    mhwlist.append(mhw[i])

mhw_df['Score']=mhwlist
mhw_df


#Visualizing data the Top 15 Teams with most wins when palying as home team 
matplotlib.rcParams['figure.figsize'] = (15, 5)
sns.barplot(data=mhw_df,x='Score',y='Teams',orient='h',palette='flare_r',saturation=0.8)
plt.title('Top 15 Teams with most wins on Home during campaign of 1986 to 2022')

Text(0.5, 1.0, 'Top 15 Teams with most wins on Home during campaign of 1986 to 2022')


#Reviewing data in home_win dataframe 
away_wins_df


#Getting data of teams with most wins when playing as away team 
away_wins_df.away_team.value_counts()

Spain                  14
Germany                11
France                 11
Brazil                 10
Mexico                  9
Croatia                 9
Netherlands             8
England                 7
Denmark                 6
Italy                   6
Switzerland             6
Uruguay                 6
Belgium                 5
Argentina               5
Senegal                 5
Costa Rica              5
Colombia                5
Morocco                 4
Ghana                   4
Japan                   4
Ecuador                 3
Paraguay                3
Romania                 3
United States           3
Türkiye                 3
IR Iran                 3
Chile                   2
Republic of Ireland     2
Serbia                  2
Nigeria                 2
Sweden                  2
Czechoslovakia          2
Saudi Arabia            2
Poland                  2
FR Yugoslavia           1
Norway                  1
Yugoslavia              1
Bulgaria                1
Soviet Union            1
Scotland                1
Jamaica                 1
Slovenia                1
Korea Republic          1
Portugal                1
Czech Republic          1
Ukraine                 1
South Africa            1
Côte d'Ivoire           1
Algeria                 1
Australia               1
Peru                    1
Tunisia                 1
Cameroon                1
Name: away_team, dtype: int64


#Storing the data in new data frame in order to visualize it 
maw=away_wins_df.away_team.value_counts().head(15)
maw_df=pd.DataFrame()
maw_df['Teams']=maw.index
mawlist=[]
for i in range(0,15):
    mawlist.append(maw[i])

maw_df['Score']=mawlist
maw_df


#Visualizing data the Top 15 Teams with most wins when playing as away team  
sns.barplot(data=maw_df,x='Score',y='Teams',orient='h',palette='crest_r',saturation=1)
plt.title('Top 15 Teams with most wins on Away during campaign of 1986 to 2022')

Text(0.5, 1.0, 'Top 15 Teams with most wins on Away during campaign of 1986 to 2022')


#plotting the graph of team with most win as home team and team with most win as away team side by side for comparison. 
plt.rcParams['figure.figsize'] = (18,8)
fig,axes=plt.subplots(1,2)
sns.barplot(data=mhw_df,x='Score',y='Teams',orient='h',palette='flare_r',saturation=0.8,ax=axes[0])
axes[0].set_title('Top 15 Teams with most wins on Home during campaign of 1986 to 2022')

sns.barplot(data=maw_df,x='Score',y='Teams',orient='h',palette='crest_r',saturation=1,ax=axes[1])
axes[1].set_title('Top 15 Teams with most wins on Away during campaign of 1986 to 2022')
plt.show()


#Reviewing data in draws dataframe 
draws_df


#Adding a new column in draws data frame as margin where margin is diff b/w home_penalty and away_penalty
draws_df["margin"]=draws_df.home_penalty-draws_df.away_penalty
draws_df


# Checking for Nan values in Margin column of draws dataframe
draws_df.margin.isna().sum()

110


# checking if above obsevation made is true
draws_df[draws_df.Round=='Group stage']


draws_df[draws_df.Round=='Group stage'].info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 110 entries, 21 to 603
Data columns (total 15 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   Year          110 non-null    int64  
 1   Host          110 non-null    object 
 2   Date          110 non-null    object 
 3   Venue         110 non-null    object 
 4   Round         110 non-null    object 
 5   home_team     110 non-null    object 
 6   away_team     110 non-null    object 
 7   home_penalty  0 non-null      float64
 8   away_penalty  0 non-null      float64
 9   Notes         0 non-null      object 
 10  home_manager  110 non-null    object 
 11  home_captain  46 non-null     object 
 12  away_manager  110 non-null    object 
 13  away_captain  46 non-null     object 
 14  margin        0 non-null      float64
dtypes: float64(3), int64(1), object(11)
memory usage: 13.8+ KB


#Dataframe of home penalty wins 
draws_df[draws_df.margin>0].info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 18 entries, 0 to 559
Data columns (total 15 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   Year          18 non-null     int64  
 1   Host          18 non-null     object 
 2   Date          18 non-null     object 
 3   Venue         18 non-null     object 
 4   Round         18 non-null     object 
 5   home_team     18 non-null     object 
 6   away_team     18 non-null     object 
 7   home_penalty  18 non-null     float64
 8   away_penalty  18 non-null     float64
 9   Notes         18 non-null     object 
 10  home_manager  18 non-null     object 
 11  home_captain  8 non-null      object 
 12  away_manager  18 non-null     object 
 13  away_captain  8 non-null      object 
 14  margin        18 non-null     float64
dtypes: float64(3), int64(1), object(11)
memory usage: 2.2+ KB


#Dataframe of away penalty wins 
draws_df[draws_df.margin<0].info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 16 entries, 7 to 558
Data columns (total 15 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   Year          16 non-null     int64  
 1   Host          16 non-null     object 
 2   Date          16 non-null     object 
 3   Venue         16 non-null     object 
 4   Round         16 non-null     object 
 5   home_team     16 non-null     object 
 6   away_team     16 non-null     object 
 7   home_penalty  16 non-null     float64
 8   away_penalty  16 non-null     float64
 9   Notes         16 non-null     object 
 10  home_manager  16 non-null     object 
 11  home_captain  11 non-null     object 
 12  away_manager  16 non-null     object 
 13  away_captain  11 non-null     object 
 14  margin        16 non-null     float64
dtypes: float64(3), int64(1), object(11)
memory usage: 2.0+ KB


#Getting data of teams with most penalty wins when playing as home team 
home_penalty_win=draws_df[draws_df.margin>0]
home_penalty_win.home_team.value_counts()

Brazil                 3
Germany                3
Argentina              2
Croatia                2
Morocco                1
Netherlands            1
Costa Rica             1
Uruguay                1
Paraguay               1
Italy                  1
Spain                  1
Republic of Ireland    1
Name: home_team, dtype: int64


#Getting data of teams with most penalty wins when playing as away team 
away_penalty_win=draws_df[draws_df.margin<0]
away_penalty_win.away_team.value_counts()

Argentina         4
Croatia           2
France            2
England           1
Russia            1
Portugal          1
Ukraine           1
Korea Republic    1
Sweden            1
Bulgaria          1
Belgium           1
Name: away_team, dtype: int64


#Storing the data in new data frame in order to visualize it 
hpw=home_penalty_win.home_team.value_counts()
hpw_list=[]
hpw_df=pd.DataFrame()
hpw_df['Home_Team']=hpw.index
for i in range(0,len(hpw)):
    hpw_list.append(hpw[i])
hpw_df['penalty_wins']=hpw_list
hpw_df


#Storing the data in new data frame in order to visualize it		
apw=away_penalty_win.away_team.value_counts()
apw_list=[]
apw_df=pd.DataFrame()
apw_df['Away_Team']=apw.index
for i in range(0,len(apw)):
    apw_list.append(apw[i]) 
apw_df['penalty_wins']=apw_list
apw_df


#Visualizing data of Teams with most peanlty wins when playing as home team and away team using subplots.
fig,axes=plt.subplots(1,2)
plt.tight_layout(pad=5)
sns.barplot(data=hpw_df,y='Home_Team',x='penalty_wins',palette='flare_r',ax=axes[0])
axes[0].set_title('Team with most penalty win on Home')
axes[0].set_ylabel(None)
sns.barplot(data=apw_df,y='Away_Team',x='penalty_wins',palette='crest_r',ax=axes[1])
axes[1].set_title('Team with most penalty win on Away')
axes[1].set_ylabel(None)

Text(678.2295454545454, 0.5, '')


#Creating a new dataset and copying the data of original dataframe into new dataframe 
data_df_1=data_df


#Replacing the West Germany with Germany in new dataframe as in October 1990 reunification happened
data_df_1['home_team']=data_df_1['home_team'].replace(['West Germany'],'Germany')
data_df_1['away_team']=data_df_1['away_team'].replace(['West Germany'],'Germany')


#Creating new columns margin
data_df_1['margin']=data_df_1.home_score-data_df_1.away_score


#Based on margins columns creating new columns as Winner
#Winner columns contain list of winning teams in that particular match based on normal score(margin)
data_df_1.loc[data_df_1.margin>0,'Winner']=data_df_1.home_team
data_df_1.loc[data_df_1.margin<0,'Winner']=data_df_1.away_team
data_df_1.loc[data_df_1.margin==0,'Winner']='Draw'


#Creating new column of penalty margin
data_df_1['penalty_margin']=data_df_1.home_penalty-data_df_1.away_penalty


#Based on penalty margin columns creating new columns as Winner
#Winner columns contain list of winning teams in that particular match based on penalty score(penalty margin)
data_df_1.loc[data_df_1.penalty_margin>0,'Winner']=data_df_1.home_team
data_df_1.loc[data_df_1.penalty_margin<0,'Winner']=data_df_1.away_team
data_df_1.loc[data_df_1.penalty_margin==0,'Winner']='Draw'


#Reviewing data in new dataframe after the above manipualtion
data_df_1


#Reviewing the new dataframe using info function
data_df_1.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 604 entries, 0 to 603
Data columns (total 23 columns):
 #   Column          Non-Null Count  Dtype  
---  ------          --------------  -----  
 0   home_team       604 non-null    object 
 1   away_team       604 non-null    object 
 2   home_score      604 non-null    int64  
 3   home_xg         128 non-null    float64
 4   home_penalty    34 non-null     float64
 5   away_score      604 non-null    int64  
 6   away_xg         128 non-null    float64
 7   away_penalty    34 non-null     float64
 8   home_manager    604 non-null    object 
 9   home_captain    284 non-null    object 
 10  away_manager    604 non-null    object 
 11  away_captain    284 non-null    object 
 12  Attendance      604 non-null    int64  
 13  Venue           604 non-null    object 
 14  Round           604 non-null    object 
 15  Date            604 non-null    object 
 16  Score           604 non-null    object 
 17  Notes           54 non-null     object 
 18  Host            604 non-null    object 
 19  Year            604 non-null    int64  
 20  margin          604 non-null    int64  
 21  Winner          604 non-null    object 
 22  penalty_margin  34 non-null     float64
dtypes: float64(5), int64(5), object(13)
memory usage: 108.7+ KB


#Understanding the Winner column of the new dataframe

data_df_1.Winner.value_counts()

Draw                      110
Brazil                     42
Germany                    40
Argentina                  37
France                     30
Spain                      24
Netherlands                23
Italy                      22
England                    20
Belgium                    19
Croatia                    17
Mexico                     14
Portugal                   13
Uruguay                    12
Denmark                     9
Sweden                      9
Colombia                    9
Switzerland                 9
Korea Republic              8
Costa Rica                  7
Japan                       7
Morocco                     6
United States               6
Romania                     6
Paraguay                    6
Nigeria                     6
Cameroon                    5
Russia                      5
Senegal                     5
Ecuador                     5
Poland                      5
Ghana                       5
Saudi Arabia                4
Türkiye                     4
Australia                   4
Bulgaria                    4
Chile                       4
Republic of Ireland         3
Côte d'Ivoire               3
Soviet Union                3
Yugoslavia                  3
Czechoslovakia              3
IR Iran                     3
Ukraine                     3
Norway                      2
FR Yugoslavia               2
Tunisia                     2
South Africa                2
Greece                      2
Serbia                      2
Czech Republic              1
Slovenia                    1
Jamaica                     1
Slovakia                    1
Algeria                     1
Bosnia and Herzegovina      1
Austria                     1
Peru                        1
Scotland                    1
Hungary                     1
Name: Winner, dtype: int64


#Storing the data in new data frame in order to visualize it 
most_wins=data_df_1[data_df_1.Winner!='Draw']
team_most_wins=pd.DataFrame()
team_most_wins['Team']=most_wins.Winner.value_counts().index
team_most_wins['No. of wins']=most_wins.Winner.value_counts().to_list()
tmw=team_most_wins.head(15)
tmw


#Visualizing data the Top 15 Teams with most wins. 
matplotlib.rcParams['figure.figsize']=(15,5)
matplotlib.rcParams['font.size']=12

ax=sns.barplot(data=tmw,y='Team',x='No. of wins',orient='h',palette='flare_r',saturation=0.9)
ax.set(title='Team with most win during the World Cup campaign of 1986 to 2022 (Top 15)')

for p in ax.patches:
    height=p.get_height()
    width=p.get_width()
    ax.text(x=width+0.1, y=p.get_y()+(height/2),s='{:.0f}'.format(width) , va='center')

plt.show()


#Using the group by on new dataframe and creating a another dataframe and storing the values in it.
#Here groupby func is used and groupby is based on rounds which is different from previously used group by function. 
group_wise_data=pd.DataFrame()
group_wise_data['No. of matches']=data_df_1.groupby(by='Round').count()['Attendance']
group_wise_data['Total_Attendance']=data_df_1.groupby(by='Round').sum()['Attendance']
group_wise_data['Average_Attendance']=group_wise_data['Total_Attendance']/group_wise_data['No. of matches']

group_wise_data['goals_per_match']=(data_df_1.groupby(by='Round').sum()['home_score']/group_wise_data['No. of matches'])+(data_df_1.groupby(by='Round').sum()['away_score']/group_wise_data['No. of matches'])
group_wise_data['penalty_goals_per_match']=(data_df_1.groupby(by='Round').sum()['home_penalty']/group_wise_data['No. of matches'])+(data_df_1.groupby(by='Round').sum()['away_penalty']/group_wise_data['No. of matches'])

group_wise_data['Round']=data_df_1.groupby(by='Round').count()['Attendance'].index

gwd_df=group_wise_data


#Viewing newly created dataframe
gwd_df['Total_goals_per_match']=gwd_df.goals_per_match+gwd_df.penalty_goals_per_match
gwd_df


# From the above dataframe creating line graph of attendance per match in different rounds/stages in World Cup
# in order to understand the trend of attendance round wise.
matplotlib.rcParams['figure.figsize']=(15,5)
sns.lineplot(data=gwd_df.sort_values(by='Average_Attendance'),x='Round',y='Average_Attendance',color='#cc0099',marker='o')
plt.show()


#same data but instead of line graph barplot is used for simpler understanding. 
matplotlib.rcParams['figure.figsize']=(10,5)
sns.barplot(data=gwd_df.sort_values(by='Average_Attendance',ascending=False),y='Round',x='Average_Attendance',
           palette='crest_r',saturation=0.8)
plt.title('Average Attendance in World Cup during the campaign of 1986 to 2022 on basis of Round')
plt.xlabel('Average Attendance')
plt.show()


#From the dataframe creating barplot of goals per match in different rounds/stages in World Cup 
sns.barplot(data=gwd_df.sort_values(by='goals_per_match',ascending=False),y='Round',x='goals_per_match',
           palette='flare_r',saturation=0.8)
plt.title('Goals per match in World Cup during the campaign of 1986 to 2022 on basis of Round')
plt.xlabel('Goals per match')
plt.show()


#From the dataframe creating barplot of penalty goals per match in different rounds/stages in World Cup 
sns.barplot(data=gwd_df.sort_values(by='penalty_goals_per_match',ascending=False),y='Round',x='penalty_goals_per_match',
           palette='Purples_r',saturation=0.8)
plt.title('Penalty Goals per match in World Cup during the campaign of 1986 to 2022 on basis of Round')
plt.xlabel('penalty goals per match')
plt.show()


#From the dataframe creating barplot of total goals per match in different rounds/stages in World Cup 
sns.barplot(data=gwd_df.sort_values(by='Total_goals_per_match',ascending=False),y='Round',x='Total_goals_per_match',
           palette='flare_r',saturation=0.8)
plt.title('Total Goals per match in World Cup during the campaign of 1986 to 2022 on basis of Round')
plt.xlabel('Total goals per match')

Text(0.5, 0, 'Total goals per match')


Finals_data_df=data_df_1[data_df_1['Round']=='Final']
fd_df=Finals_data_df


# From the above dataframe creating line graph of attendance in finals host wise 
matplotlib.rcParams['figure.figsize']=(20,5)
sns.lineplot(data=fd_df.iloc[::-1],x='Host',y='Attendance',marker='s',color='#0000A5')
plt.title('World Cup Attendance in Finals')
plt.xlabel(None)
plt.ylabel(None)
plt.show()


#From the dataframe creating line graph of attendance in finals year wise which will same graph but interpretation is diff 
sns.lineplot(data=fd_df,x='Year',y='Attendance',marker='s',color='#0000A5')
plt.title('World Cup Attendance in Finals on basis of Year')
plt.ylabel(None)
plt.show()


data_df_1.loc[data_df_1.margin>0,'Winning_manager']=data_df_1.home_manager
data_df_1.loc[data_df_1.margin<0,'Winning_manager']=data_df_1.away_manager
data_df_1.loc[data_df_1.margin==0,'Winning_manager']='No Win'
data_df_1.loc[data_df_1.penalty_margin>0,'Winning_manager']=data_df_1.home_manager
data_df_1.loc[data_df_1.penalty_margin<0,'Winning_manager']=data_df_1.away_manager
data_df_1.loc[data_df_1.penalty_margin==0,'Winning_manager']='No Win'

data_df_1.loc[data_df_1.margin>0,'Winning_captain']=data_df_1.home_captain
data_df_1.loc[data_df_1.margin<0,'Winning_captain']=data_df_1.away_captain
data_df_1.loc[data_df_1.margin==0,'Winning_captain']='No Win'
data_df_1.loc[data_df_1.penalty_margin>0,'Winning_captain']=data_df_1.home_captain
data_df_1.loc[data_df_1.penalty_margin<0,'Winning_captain']=data_df_1.away_captain
data_df_1.loc[data_df_1.penalty_margin==0,'Winning_captain']='No Win'


data_df_1


#Reviewing the data using info func 
data_df_1.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 604 entries, 0 to 603
Data columns (total 25 columns):
 #   Column           Non-Null Count  Dtype  
---  ------           --------------  -----  
 0   home_team        604 non-null    object 
 1   away_team        604 non-null    object 
 2   home_score       604 non-null    int64  
 3   home_xg          128 non-null    float64
 4   home_penalty     34 non-null     float64
 5   away_score       604 non-null    int64  
 6   away_xg          128 non-null    float64
 7   away_penalty     34 non-null     float64
 8   home_manager     604 non-null    object 
 9   home_captain     284 non-null    object 
 10  away_manager     604 non-null    object 
 11  away_captain     284 non-null    object 
 12  Attendance       604 non-null    int64  
 13  Venue            604 non-null    object 
 14  Round            604 non-null    object 
 15  Date             604 non-null    object 
 16  Score            604 non-null    object 
 17  Notes            54 non-null     object 
 18  Host             604 non-null    object 
 19  Year             604 non-null    int64  
 20  margin           604 non-null    int64  
 21  Winner           604 non-null    object 
 22  penalty_margin   34 non-null     float64
 23  Winning_manager  604 non-null    object 
 24  Winning_captain  348 non-null    object 
dtypes: float64(5), int64(5), object(15)
memory usage: 118.1+ KB


# Creating a new dataframe of only with manager having wins 
manager_wins=data_df_1[data_df_1.Winning_manager!='No Win']


#Viewing the new dataframe 
manager_wins


#Getting data of manager with most wins 
manager_wins.Winning_manager.value_counts()

Luiz Felipe Scolari        16
Didier Deschamps           14
Joachim Löw                12
Carlos Alberto Parreira    11
Zlatko Dalić               10
                           ..
Juan Antonio Pizzi          1
Gernot Rohr                 1
Ricardo La Volpe            1
Karel Brückner              1
Jose Torres                 1
Name: Winning_manager, Length: 170, dtype: int64


#Storing the data in new data frame in order to visualize it 
most_win_manager=pd.DataFrame()
most_win_manager['Manager']=manager_wins.Winning_manager.value_counts().head(20).index
most_win_manager['Country_managed']=[
"Brazil",
"France",
"Germany",
["South Africa,Brazil"],
"Croatia",
"Germany",
"Uruguay",
"Argentina",
"Netherlands",
["Australia, Korea Republic, Netherlands"],
["United States,Germany"],
"England",
"Belgium",
["CÃ´te d'Ivoire, Morocco, France"],
"France",
"Spain",
"Argentina",
"Colombia",
"Argentina",
"Italy",
]
most_win_manager['Wins']=manager_wins.Winning_manager.value_counts().head(20).to_list()
most_win_manager


#Visualizing the data 
matplotlib.rcParams['figure.figsize']=(20,8)
matplotlib.rcParams['font.size']=14
sns.barplot(data=most_win_manager.head(15),x='Wins',y='Manager',palette='mako',saturation=0.8)
plt.ylabel(None) 
plt.title('Most Successful Managers during the World-Cup champaign of 1986 to 2022 (Top 15)')
plt.show()


#reviewing data_df_1 dataframe for getting info of captains with most wins 
data_df_1.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 604 entries, 0 to 603
Data columns (total 25 columns):
 #   Column           Non-Null Count  Dtype  
---  ------           --------------  -----  
 0   home_team        604 non-null    object 
 1   away_team        604 non-null    object 
 2   home_score       604 non-null    int64  
 3   home_xg          128 non-null    float64
 4   home_penalty     34 non-null     float64
 5   away_score       604 non-null    int64  
 6   away_xg          128 non-null    float64
 7   away_penalty     34 non-null     float64
 8   home_manager     604 non-null    object 
 9   home_captain     284 non-null    object 
 10  away_manager     604 non-null    object 
 11  away_captain     284 non-null    object 
 12  Attendance       604 non-null    int64  
 13  Venue            604 non-null    object 
 14  Round            604 non-null    object 
 15  Date             604 non-null    object 
 16  Score            604 non-null    object 
 17  Notes            54 non-null     object 
 18  Host             604 non-null    object 
 19  Year             604 non-null    int64  
 20  margin           604 non-null    int64  
 21  Winner           604 non-null    object 
 22  penalty_margin   34 non-null     float64
 23  Winning_manager  604 non-null    object 
 24  Winning_captain  348 non-null    object 
dtypes: float64(5), int64(5), object(15)
memory usage: 118.1+ KB


data_df_1.groupby('Year').count()[['Winning_captain','home_captain','away_captain']]


data_df.groupby('Year').count()[['home_captain','away_captain']]


#Importing the newly created dataset 
urlretrieve('https://raw.githubusercontent.com/meetth77/jovian_practice/main/Worldcup_matches_edited_1.csv','./cleaned_data.csv')
os.listdir('./')

['.git',
 '.jovianrc',
 'zerotopandas-course-project-Copy5.ipynb',
 '.ipynb_checkpoints',
 'new.csv',
 'wc_cleaned.csv',
 'cleaned_data.csv']


#Reading the data and reading the data into newly created new dataframe 
cleaned_data_df=pd.read_csv('./cleaned_data.csv')
cleaned_data_df.pop('Unnamed: 0')
cleaned_data_df


#reviewing the new dataframe 
cleaned_data_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 604 entries, 0 to 603
Data columns (total 25 columns):
 #   Column           Non-Null Count  Dtype  
---  ------           --------------  -----  
 0   home_team        604 non-null    object 
 1   away_team        604 non-null    object 
 2   home_score       604 non-null    int64  
 3   home_xg          128 non-null    float64
 4   home_penalty     34 non-null     float64
 5   away_score       604 non-null    int64  
 6   away_xg          128 non-null    float64
 7   away_penalty     34 non-null     float64
 8   home_manager     604 non-null    object 
 9   away_manager     604 non-null    object 
 10  home_captain     284 non-null    object 
 11  away_captain     284 non-null    object 
 12  Attendance       604 non-null    int64  
 13  Venue            604 non-null    object 
 14  Round            604 non-null    object 
 15  Date             604 non-null    object 
 16  Score            604 non-null    object 
 17  Notes            54 non-null     object 
 18  Host             604 non-null    object 
 19  margin           604 non-null    int64  
 20  penalty_margin   34 non-null     float64
 21  Year             604 non-null    int64  
 22  Winner           604 non-null    object 
 23  Winning_manager  604 non-null    object 
 24  Winning_captain  604 non-null    object 
dtypes: float64(5), int64(5), object(15)
memory usage: 118.1+ KB


#Storing the data in new data frame in order to visualize it 
ccdd=cleaned_data_df[cleaned_data_df.Winning_captain!='Draw']
ccd_df=pd.DataFrame()
ccd_df['Captain']=ccdd.Winning_captain.value_counts().head(20).index
ccd_df['Team']=[
    "France",
    "Argentina",
    "Argentina",
    "Germany",
    "Brazil",
    "Croatia",
    "Germany",
    "Brazil",
    "Brazil",
    "France",
    "England",
    "Spain",
    "Germany",
    "Netherlands",
    "Belgium",
    "Netherlands",
    "Uruguay",
    "Italy",
    "Italy",
    "Croatia",
]

ccd_df['Wins']=ccdd.Winning_captain.value_counts().head(20).to_list()
ccd_df


#Visualizing the above data
sns.barplot(data=ccd_df.head(15),y="Captain",x="Wins",orient='h',palette='crest_r',saturation=0.8)
plt.ylabel('Captains')
plt.title('Most Successful Captains during the World-Cup champaign of 1986 to 2022 (Top 15)')
plt.show()


#Creating a new dataset containing data of final round of every World Cup 
worldcup_winner=cleaned_data_df[cleaned_data_df.Round=='Final']
worldcup_winner=worldcup_winner[['Date','Year','Venue','Host','home_team','away_team','Score','Winner',
                                 'Notes','home_manager','away_manager','Winning_manager',
                                 'home_captain','away_captain','Winning_captain','Attendance',
                                 'home_score','away_score','home_penalty','away_penalty','home_xg','away_xg',]]

worldcup_winner


#Analyzing data of Winner, finding out team with most World Cups 
worldcup_winner.Winner.value_counts()
wcw_df=pd.DataFrame()
wcw_df['Team']=worldcup_winner.Winner.value_counts().index
wcw_df['Wins']=worldcup_winner.Winner.value_counts().to_list()
wcw_df


#Visualizing the above data 
sns.barplot(data=wcw_df,x='Team',y='Wins',palette='rocket')
plt.title('World Cup Winning Teams from 1986 to 2022')
plt.xlabel(None)
plt.show()


#probability of finals going in overtime or in penalties
prob_otpen=(worldcup_winner.Notes.notna().sum()/worldcup_winner.Year.count())

print('Probability World Cup Finals going in overtime or penalties is {:0.2f}%.'.format(prob_otpen*100))

Probability World Cup Finals going in overtime or penalties is 50.00%.


prob_pen=(worldcup_winner.Notes.notna() & worldcup_winner.home_penalty.notna()).sum()/worldcup_winner.Year.count()

print('Probability World Cup Finals going in penalties is {:0.2f}%.'.format(prob_pen*100))

Probability World Cup Finals going in penalties is 30.00%.


prob_ot=(worldcup_winner.Notes.notna() & worldcup_winner.home_penalty.isna()).sum()/worldcup_winner.Year.count()

print('Probability World Cup Finals going in overtime/extratime is {:0.2f}%.'.format(prob_ot*100))

Probability World Cup Finals going in overtime/extratime is 20.00%.


#Creating a new dataframe as worldcup_winner_1 using previous dataframe
#Replacing Nan values with zero in order to perform addition of that column and creating new columns
worldcup_winner_1=worldcup_winner
worldcup_winner_1['home_score']=worldcup_winner['home_score'].fillna(0)
worldcup_winner_1['away_score']=worldcup_winner['away_score'].fillna(0)
worldcup_winner_1['home_penalty']=worldcup_winner['home_penalty'].fillna(0)
worldcup_winner_1['away_penalty']=worldcup_winner['away_penalty'].fillna(0)
worldcup_winner_1['Total_goals']=worldcup_winner.home_score+worldcup_winner.away_score+worldcup_winner.home_penalty+worldcup_winner.away_penalty
worldcup_winner_1


#Visualizing the data Total goals scored in finals of every World Cup year wise 
matplotlib.rcParams['figure.figsize']=(20,5)
sns.lineplot(data=worldcup_winner_1[::-1], x='Host', y='Total_goals',marker='s',color='#B048B5')
plt.title('Total goals in finals game of every FIFA World Cup from 1986 to 2022 (Including Penalties)')
plt.xlabel(None)
plt.ylabel('Goals')
plt.show()


#Visualizing the data Total goals scored in finals of every World Cup Host wise 
sns.lineplot(data=worldcup_winner_1[::-1], x='Year', y='Total_goals',marker='s',color='#B048B5')
plt.title('Total goals in finals game of every FIFA World Cup from 1986 to 2022 (Including Penalties)')
plt.ylabel('Goals')
plt.show()


#worldcup_winner
worldcup_winner


worldcup_winner=worldcup_winner.replace(['West Germany'],'Germany')


urlretrieve('https://raw.githubusercontent.com/meetth77/jovian_practice/main/world_cup_winner.csv','./winner_data.csv')
winner_data_df_1=pd.read_csv('./winner_data.csv')
winner_data_df_1


# Merging the worldcup_winner and winner_data_df_1 to view the combined dataset which provides more insights 
worldcup_winner=worldcup_winner.rename(columns={'Winner':'Champion'})
worldcup_winner_df=pd.merge(worldcup_winner,winner_data_df_1,on=['Year','Champion'])
worldcup_winner_df


#Creating a dataset containing team with most apperance in finals and visualizing it 
noha=worldcup_winner_df.Champion.to_list()+worldcup_winner_df.Champion.to_list()
matplotlib.rcParams['figure.figsize'] = (8,4)
sns.histplot(y=noha,color='#0000CD')
plt.xlabel('No of apperances in final')
plt.ylabel('Teams')

Text(0, 0.5, 'Teams')


worldcup_winner_df.rename(columns={'Goals':'top_scorer_goals'})


worldcup_winner_df[['Year','Host','Champion','Runner_Up','Winning_captain',
                    'Winning_manager','Attendance','Score','Notes','TopScorer']]


#Creating data of teams most apperance in World Cup 
teams_played_df=pd.DataFrame()
teams_played_df['Team']=["Germany","Brazil","Argentina","France","England","Spain","Italy","Netherlands","Belgium",
"Mexico","Korea Republic","Croatia","United States","Uruguay","Portugal","Japan","Cameroon","Sweden","Denmark",
"Switzerland","Nigeria","Costa Rica","Paraguay","Morocco","Colombia","Saudi Arabia","Poland","Australia","Ghana",
"IR Iran","Tunisia","Russia","Bulgaria","Romania","Ecuador","Republic of Ireland","Senegal","Chile","Algeria","Greece",
"Serbia","South Africa","Scotland","Côte d'Ivoire","Türkiye","Norway","Soviet Union","Egypt","Austria","Canada",
"Slovenia","Honduras","Ukraine","Czechoslovakia","Yugoslavia","FR Yugoslavia","Slovakia","Togo","Wales","Jamaica",
"Bolivia","United Arab Emirates","New Zealand","Iraq","Bosnia and Herzegovina","Trinidad and Tobago","Northern Ireland",
"Qatar","China PR","Czech Republic","Panama","Iceland","Peru","Serbia and Montenegro","Korea DPR","Hungary","Angola",
]
teams_played_df['matches_played']=teams_df.value_counts().to_list()
teams_played_df


print('Number of teams/countries participated in world cup from the year 1986 to 2022 are: {}.'.format(len(teams)))

Number of teams/countries participated in world cup from the year 1986 to 2022 are: 77.


print('Total Number of matches played in world cup from the year 1986 to 2022 are: {}.'.format(matches_df.home_team.count()))

Total Number of matches played in world cup from the year 1986 to 2022 are: 604.


teams_sorted=teams
teams_sorted.sort()
print('List of Countries/team appearing in World Cup from 1986 to 2022 are:\n',teams_sorted)

List of Countries/team appearing in World Cup from 1986 to 2022 are:
 ['Algeria', 'Angola', 'Argentina', 'Australia', 'Austria', 'Belgium', 'Bolivia', 'Bosnia and Herzegovina', 'Brazil', 'Bulgaria', 'Cameroon', 'Canada', 'Chile', 'China PR', 'Colombia', 'Costa Rica', 'Croatia', 'Czech Republic', 'Czechoslovakia', "Côte d'Ivoire", 'Denmark', 'Ecuador', 'Egypt', 'England', 'FR Yugoslavia', 'France', 'Germany', 'Ghana', 'Greece', 'Honduras', 'Hungary', 'IR Iran', 'Iceland', 'Iraq', 'Italy', 'Jamaica', 'Japan', 'Korea DPR', 'Korea Republic', 'Mexico', 'Morocco', 'Netherlands', 'New Zealand', 'Nigeria', 'Northern Ireland', 'Norway', 'Panama', 'Paraguay', 'Peru', 'Poland', 'Portugal', 'Qatar', 'Republic of Ireland', 'Romania', 'Russia', 'Saudi Arabia', 'Scotland', 'Senegal', 'Serbia', 'Serbia and Montenegro', 'Slovakia', 'Slovenia', 'South Africa', 'Soviet Union', 'Spain', 'Sweden', 'Switzerland', 'Togo', 'Trinidad and Tobago', 'Tunisia', 'Türkiye', 'Ukraine', 'United Arab Emirates', 'United States', 'Uruguay', 'Wales', 'Yugoslavia']


teams_played_df.head(15)


matplotlib.rcParams['font.size']=14
matplotlib.rcParams['figure.figsize']=(15,5)
sns.barplot(data=teams_played_df.head(15),y='Team',x='matches_played',palette='crest_r',saturation=0.8)
plt.title('Teams with most participation in World Cup from 1986 to 2022 (Top 15)')
plt.ylabel('Teams/Countries')
plt.show()


from plotly.offline import iplot
import plotly.graph_objs as go
data=dict(type='choropleth',
          locations=teams_played_df.Team,
          locationmode='country names',
          colorscale='teal',
          z=teams_played_df.matches_played,
          colorbar={'title':'No of Matches played'}
        )
layout=dict(geo={'scope':'world'})
chmap=go.Figure(data=[data],layout=layout)
iplot(chmap)


data=dict(type='choropleth',
          locations=teams_played_df.Team,
          locationmode='country names',
          colorscale='teal',
          z=teams_played_df.matches_played,
          colorbar={'title':'No of Matches played'}
        )
layout=dict(geo={'scope':'asia'})
chmap=go.Figure(data=[data],layout=layout)
iplot(chmap)


data=dict(type='choropleth',
          locations=teams_played_df.Team,
          locationmode='country names',
          colorscale='teal',
          z=teams_played_df.matches_played,
          colorbar={'title':'No of Matches played'}
        )
layout=dict(geo={'scope':'africa'})
chmap=go.Figure(data=[data],layout=layout)
iplot(chmap)


matches_df[matches_df.Round=='Final'][['Host','Year']][::-1]


matplotlib.rcParams['figure.figsize']=(20,5)
matplotlib.rcParams['font.size']=14
sns.lineplot(x=ywd.Host ,y=ywd.Attendance,color='#5900b3')
plt.title('Total Attendance Host Wise')
plt.ylabel("Attendance (in millions)")
plt.xlabel('Host Nation')
plt.show()


sns.lineplot(x=ywd.index,y=ywd.Attendance,color='#5900b3',marker='s')
plt.title('Total Attendance Year Wise')
plt.ylabel("Attendance (in millions)")
plt.xlabel('Year')
plt.show()


ywd[['Host','No_of_matches','No_of_teams','Attendance','Average_attendance']]


plt.plot(ywd.Host,ywd.Average_attendance,color='#5900b3')
plt.title('Attendance per match')
plt.ylabel("Attendance")
plt.xlabel('Host Nation')
plt.show()


plt.plot(ywd.index,ywd.Average_attendance,color='#5900b3',marker='o')
plt.title('Attendance per match')
plt.ylabel("Attendance")
plt.xlabel('Host Nation')
plt.show()


gwd_df[['No. of matches','Total_Attendance','Average_Attendance']]


sns.lineplot(data=gwd_df.sort_values(by='Average_Attendance'),x='Round',y='Average_Attendance',color='#5900b3')
plt.title('Trend of attendance Round/Stage Wise')
plt.ylabel('Attendance')
plt.show()


matplotlib.rcParams['figure.figsize']=(15,4)
sns.barplot(data=gwd_df.sort_values(by='Average_Attendance',ascending=False),y='Round',x='Average_Attendance',
           palette='mako',saturation=0.8)
plt.title('Average Attendance in World Cup (1986 to 2022) on basis of Round')
plt.xlabel('Attendance')
plt.ylabel(None)
plt.show()


ywd[['Host','Total_home_goals','Total_away_goals','Total_home_penalty','Total_away_penalty','normal_goals',
     'Total_penalty','Total_goals']]


matplotlib.rcParams['figure.figsize'] = (20, 5)
sns.lineplot(data=ywd,x=ywd.index,y='Total_home_goals',marker='o',color='#5900b3')
sns.lineplot(data=ywd,x=ywd.index,y='Total_away_goals',marker='s',color='turquoise')
plt.title('Trend of goals scored by Year wise')
plt.xlabel('Year')
plt.ylabel('No. of goals scored')
plt.legend(['Home goals','Away goals'])
plt.show()


plt.plot(ywd.Host,ywd.Total_home_goals,color='#5900b3')
plt.plot(ywd.Host,ywd.Total_away_goals,color='turquoise')
plt.title('Trend of goals scored Host wise')
plt.xlabel('Host Nation')
plt.ylabel('No. of goals scored')
plt.legend(['Home goals','Away goals'])
plt.show()


fig,axes=plt.subplots(1,2)
sns.barplot(data=ywd,y=ywd.index,x='Total_home_goals',orient='h',palette='mako',saturation=10,ax=axes[0])
axes[0].set_title('Home Goals by years')
axes[0].set_xlabel('Home Goals')

sns.barplot(data=ywd,y=ywd.index,x='Total_away_goals',orient='h',palette='rocket',saturation=10,ax=axes[1])
axes[1].set_title('Away Goals by years')
axes[1].set_xlabel('Away Goals')

plt.show()


sns.lineplot(data=ywd,x=ywd.index,y='Total_home_penalty',marker='s',color='#5900b3')
sns.lineplot(data=ywd,x=ywd.index,y='Total_away_penalty',marker='o',color='turquoise')
plt.title('Trend of Penalty scored year wise')
plt.xlabel('Year')
plt.ylabel('No. of Penalty')
plt.legend(['Home penalty','Away penalty'])
plt.show()


sns.lineplot(data=ywd,x='Host',y='Total_home_penalty',color='#5900b3')
sns.lineplot(data=ywd,x='Host',y='Total_away_penalty',color='turquoise')
plt.title('Trend of Penalty scored Host wise[1986 to 2022]')
plt.xlabel('Year')
plt.ylabel('No. of Penalty')
plt.legend(['Home penalty','Away penalty'])
plt.show()


sns.lineplot(data=ywd,x='Host',y='normal_goals',color='#5900b3')
plt.title('Trend of Normal goals scored Host wise[1986 to 2022]')
plt.xlabel('Host Nation')
plt.ylabel('No. of goals')
plt.show()


sns.lineplot(data=ywd,x='Host',y='Total_penalty',color='#5900b3')
plt.title('Trend of penalty goals scored Host wise[1986 to 2022]')
plt.xlabel('Year')
plt.ylabel('No. of penalty')
plt.show()


sns.lineplot(data=ywd,x='Host',y='Total_goals',color='#5900b3')
plt.title('Trend of Total Goals scored Host wise[1986 to 2022]')
plt.xlabel('Year')
plt.ylabel('No. of Goals')
plt.show()


gwd_df[['No. of matches','goals_per_match','penalty_goals_per_match','Total_goals_per_match']]


sns.barplot(data=gwd_df.sort_values('goals_per_match',ascending=False),y='Round',x='goals_per_match',orient='h',
            palette='mako')
plt.title('Trend of Goals per match based on Round/Stage')
plt.xlabel('No of goals')
plt.show()


sns.barplot(data=gwd_df.sort_values('penalty_goals_per_match',ascending=False),y='Round',x='penalty_goals_per_match',
            orient='h',palette='mako')
plt.title('Trend of Penalty scored per match based on Round/Stage')
plt.xlabel('No of penalty scored')
plt.show()


sns.barplot(data=gwd_df.sort_values('Total_goals_per_match',ascending=False),y='Round',x='Total_goals_per_match',
            orient='h',palette='mako')
plt.title('Trend of Total goals scored per match based on Round/Stage')
plt.xlabel('No of goals scored')
plt.show()


worldcup_winner_df[['Year','Champion','Runner_Up','Score','Attendance','Notes']][::-1]


sns.lineplot(data=worldcup_winner[::-1],x='Host',y='Attendance',color='#5900b3')
plt.title('Trend of Attendance in Final stage of World Cup based on Host Nation')
plt.xlabel('Host Nation')
plt.show()


sns.lineplot(data=worldcup_winner[::-1],x='Year',y='Attendance',color='#5900b3',marker='s')
plt.title('Trend of Attendance in Final stage of World Cup based on Years')
plt.xlabel('Year')
plt.show()


matplotlib.rcParams['figure.figsize']=(20,5)
sns.lineplot(data=worldcup_winner_1[::-1], x='Host', y='Total_goals',color='#5900b3')
plt.title('Total goals in final game of every FIFA World Cup from 1986 to 2022 (Including Penalties)')
plt.xlabel(None)
plt.ylabel('Goals')
plt.show()


sns.lineplot(data=worldcup_winner_1[::-1], x='Year', y='Total_goals',marker='s',color='#5900b3')
plt.title('Total goals in final game of every FIFA World Cup (Including Penalties)')
plt.ylabel('Goals')
plt.show()


worldcup_winner[['Year','Host','Champion','Score','Notes']]


sns.histplot(x=noha,color='#008080')
plt.title('Teams with most appearance in finals of World Cup')
plt.xlabel('Teams')
plt.ylabel('No of Appearances')
plt.show()


wcw_df


sns.barplot(data=wcw_df,x='Team',y='Wins',palette='crest_r')
plt.title('World Cup Winning Teams from 1986 to 2022')
plt.xlabel(None)
plt.show()


mhw_df


matplotlib.rcParams['figure.figsize'] = (15, 5)
sns.barplot(data=mhw_df,x='Score',y='Teams',orient='h',palette='crest_r',saturation=0.8)
plt.title('Teams with most wins as Home Team during the World Cup campaign of 1986 to 2022(Top 15)')
plt.xlabel('Wins')
plt.ylabel(None)
plt.show()


maw_df


sns.barplot(data=maw_df,x='Score',y='Teams',orient='h',palette='crest_r',saturation=1)
plt.title('Teams with most wins as Away Team during the World Cup campaign of 1986 to 2022(Top 15)')
plt.xlabel('Wins')
plt.ylabel(None)
plt.show()

tmw


ax=sns.barplot(data=tmw,y='Team',x='No. of wins',orient='h',palette='crest_r',saturation=0.9)
ax.set(title='Most Successful team during the World Cup campaign of 1986 to 2022 (Top 15)',ylabel=None,xlabel='Wins')


for p in ax.patches:
    height=p.get_height()
    width=p.get_width()
    ax.text(x=width+0.1, y=p.get_y()+(height/2),s='{:.0f}'.format(width) , va='center')
plt.show()


most_win_manager.head(20)


matplotlib.rcParams['figure.figsize']=(20,8)
matplotlib.rcParams['font.size']=16
sns.barplot(y=most_win_manager.head(15).Manager,x=most_win_manager.head(15).Wins,palette='mako',saturation=0.8)
plt.ylabel('Managers') 
plt.title('Most Successful Managers during the World-Cup champaign of 1986 to 2022 (Top 15)')
plt.show()


ccd_df.head(10)


sns.barplot(y=ccd_df.head(15).Captain+' ('+ccd_df.head(15).Team+')',x=ccd_df.head(15).Wins,orient='h',
            palette='mako',saturation=0.8)
plt.ylabel('Players as Captains (Team Captained)')
plt.title('Most Successful Captains during the World-Cup champaign of 1986 to 2022 (Top 15)')
plt.show()


wcw_df_1=worldcup_winner_df[['Year','Host','Champion','Winning_captain','Winning_manager','TopScorer','Goals']][::-1]
wcw_df_1.rename(columns={'Goals':'Topscorer_goals'},inplace=True)
wcw_df_1


wcw_df_2=wcw_df_1.astype({'Year':'str'})
wcw_df_2=wcw_df_2.sort_values('Topscorer_goals',ascending=False)
sns.barplot(y=wcw_df_2.Year+' | '+wcw_df_2.Host+' | '+wcw_df_2.TopScorer,x=wcw_df_2.Topscorer_goals,palette='mako')
plt.ylabel('Year | Host Nation | Top Scorer')
plt.xlabel('Goals')
plt.title('Top scorer goals based on Host Nation')
plt.show()


import jovian


jovian.commit()

	No_of_matches	No_of_teams	Attendance	Average_attendance	Total_home_goals	Total_away_goals	Total_home_penalty	Total_away_penalty	Host	normal_goals	Total_penalty	Total_goals	Years
Year
1986	52	24	2394031	46039.057692	74	58	11.0	10.0	Mexico	132	21.0	153.0	1986
1990	52	24	2516215	48388.750000	67	48	14.0	14.0	Italy	115	28.0	143.0	1990
1994	52	24	3587538	68991.115385	83	58	8.0	10.0	United States	141	18.0	159.0	1994
1998	64	32	2903477	45366.828125	98	73	11.0	9.0	France	171	20.0	191.0	1998
2002	64	32	2705337	42270.890625	89	72	6.0	7.0	Korea Republic, Japan	161	13.0	174.0	2002
2006	64	32	3352605	52384.453125	86	61	10.0	11.0	Germany	147	21.0	168.0	2006
2010	64	32	3178856	49669.625000	76	69	9.0	5.0	South Africa	145	14.0	159.0	2010
2014	64	32	3429873	53591.765625	81	90	14.0	12.0	Brazil	171	26.0	197.0	2014
2018	64	32	3031768	47371.375000	91	78	12.0	14.0	Russia	169	26.0	195.0	2018
2022	64	32	3404252	53191.437500	101	71	15.0	11.0	Qatar	172	26.0	198.0	2022

	No_of_matches	No_of_teams	Attendance	Average_attendance	Total_home_goals	Total_away_goals	Total_home_penalty	Total_away_penalty	Host	normal_goals	Total_penalty	Total_goals	Years
Year
1986	52	24	2394031	46039.057692	74	58	11.0	10.0	Mexico	132	21.0	153.0	1986
1990	52	24	2516215	48388.750000	67	48	14.0	14.0	Italy	115	28.0	143.0	1990
1994	52	24	3587538	68991.115385	83	58	8.0	10.0	United States	141	18.0	159.0	1994
1998	64	32	2903477	45366.828125	98	73	11.0	9.0	France	171	20.0	191.0	1998
2002	64	32	2705337	42270.890625	89	72	6.0	7.0	Korea Republic, Japan	161	13.0	174.0	2002
2006	64	32	3352605	52384.453125	86	61	10.0	11.0	Germany	147	21.0	168.0	2006
2010	64	32	3178856	49669.625000	76	69	9.0	5.0	South Africa	145	14.0	159.0	2010
2014	64	32	3429873	53591.765625	81	90	14.0	12.0	Brazil	171	26.0	197.0	2014
2018	64	32	3031768	47371.375000	91	78	12.0	14.0	Russia	169	26.0	195.0	2018
2022	64	32	3404252	53191.437500	101	71	15.0	11.0	Qatar	172	26.0	198.0	2022

	No_of_matches	No_of_teams	Attendance	Average_attendance	Total_home_goals	Total_away_goals	Total_home_penalty	Total_away_penalty	Host	normal_goals	Total_penalty	Total_goals	Years
Year
1986	52	24	2394031	46039.057692	74	58	11.0	10.0	Mexico	132	21.0	153.0	1986
1990	52	24	2516215	48388.750000	67	48	14.0	14.0	Italy	115	28.0	143.0	1990
1994	52	24	3587538	68991.115385	83	58	8.0	10.0	United States	141	18.0	159.0	1994
1998	64	32	2903477	45366.828125	98	73	11.0	9.0	France	171	20.0	191.0	1998
2002	64	32	2705337	42270.890625	89	72	6.0	7.0	Korea Republic, Japan	161	13.0	174.0	2002
2006	64	32	3352605	52384.453125	86	61	10.0	11.0	Germany	147	21.0	168.0	2006
2010	64	32	3178856	49669.625000	76	69	9.0	5.0	South Africa	145	14.0	159.0	2010
2014	64	32	3429873	53591.765625	81	90	14.0	12.0	Brazil	171	26.0	197.0	2014
2018	64	32	3031768	47371.375000	91	78	12.0	14.0	Russia	169	26.0	195.0	2018
2022	64	32	3404252	53191.437500	101	71	15.0	11.0	Qatar	172	26.0	198.0	2022

	No. of matches	Total_Attendance	Average_Attendance	goals_per_match	penalty_goals_per_match	Round	Total_goals_per_match
Round
Final	10	826631	82663.100000	2.700000	1.9000	Final	4.600000
Group stage	444	21329631	48039.709459	2.502252	0.0000	Group stage	2.502252
Quarter-finals	40	2319091	57977.275000	2.175000	2.3250	Quarter-finals	4.500000
Round of 16	80	4102200	51277.500000	2.700000	0.9375	Round of 16	3.637500
Semi-finals	20	1390159	69507.950000	2.250000	1.3000	Semi-finals	3.550000
Third-place match	10	536240	53624.000000	3.800000	0.0000	Third-place match	3.800000

	No. of matches	Total_Attendance	Average_Attendance
Round
Final	10	826631	82663.100000
Group stage	444	21329631	48039.709459
Quarter-finals	40	2319091	57977.275000
Round of 16	80	4102200	51277.500000
Semi-finals	20	1390159	69507.950000
Third-place match	10	536240	53624.000000

	home_team	away_team	home_score	home_xg	home_penalty	away_score	away_xg	away_penalty	home_manager	home_captain	...	Attendance	Venue	Round	Date	Score	Notes	Host	Year	Unnamed: 20	Unnamed: 21
0	Argentina	France	3	3.3	4.0	3	2.2	2.0	Lionel Scaloni	Lionel Messi	...	88966	Lusail Iconic Stadium, Lusail	Final	12/18/2022	(4) 3–3 (2)	Argentina won on penalty kicks following extra...	Qatar	2022	NaN	NaN
1	Croatia	Morocco	2	0.7	NaN	1	1.2	NaN	Zlatko Dalić	Luka Modrić	...	44137	Khalifa International Stadium, Doha	Third-place match	12/17/2022	2–1	NaN	Qatar	2022	NaN	NaN
2	France	Morocco	2	2.0	NaN	0	0.9	NaN	Didier Deschamps	Hugo Lloris	...	68294	Al Bayt Stadium, Al Khor	Semi-finals	12/14/2022	2–0	NaN	Qatar	2022	NaN	NaN
3	Argentina	Croatia	3	2.3	NaN	0	0.5	NaN	Lionel Scaloni	Lionel Messi	...	88966	Lusail Iconic Stadium, Lusail	Semi-finals	12/13/2022	3–0	NaN	Qatar	2022	NaN	NaN
4	Morocco	Portugal	1	1.4	NaN	0	0.9	NaN	Hoalid Regragui	Romain Saïss	...	44198	Al Thumama Stadium, ath-Thumāma	Quarter-finals	12/10/2022	1–0	NaN	Qatar	2022	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
599	Argentina	Korea Republic	3	NaN	NaN	1	NaN	NaN	Carlos Bilardo	Diego Maradona	...	60000	Estadio Olï¿½mpico Universitario, Mexico City	Group stage	6/2/1986	3–1	NaN	Mexico	1986	NaN	NaN
600	Morocco	Poland	0	NaN	NaN	0	NaN	NaN	Jose Faria	Ezzaki Badou	...	19900	Estadio Universitario, Monterrey	Group stage	6/2/1986	0–0	NaN	Mexico	1986	NaN	NaN
601	Spain	Brazil	0	NaN	NaN	1	NaN	NaN	Miguel Munoz	José Antonio Camacho	...	35748	Jalisco, Guadalajara	Group stage	6/1/1986	0–1	NaN	Mexico	1986	NaN	NaN
602	Canada	France	0	NaN	NaN	1	NaN	NaN	Tony Waiters	Bruce Wilson	...	65500	Nou Camp - Estadio Leï¿½n, Leon	Group stage	6/1/1986	0–1	NaN	Mexico	1986	NaN	NaN
603	Bulgaria	Italy	1	NaN	NaN	1	NaN	NaN	Ivan Vutsov	Georgi Dimitrov	...	96000	Estadio Azteca, Mexico City	Group stage	5/31/1986	1–1	NaN	Mexico	1986	NaN	NaN

	Year	Host	Date	Venue	Round	home_team	away_team	home_penalty	away_penalty	Notes	home_manager	home_captain	away_manager	away_captain	margin
21	2022	Qatar	12/1/2022	Ahmed bin Ali Stadium, Umm al-Afā'ī	Group stage	Croatia	Belgium	NaN	NaN	NaN	Zlatko Dalić	Luka Modrić	Roberto Martínez	Kevin De Bruyne	NaN
32	2022	Qatar	11/28/2022	Al Janoub Stadium, Al Wakrah	Group stage	Cameroon	Serbia	NaN	NaN	NaN	Rigobert Song	Eric Maxim Choupo-Moting	Dragan Stojković	Dušan Tadić	NaN
39	2022	Qatar	11/27/2022	Al Bayt Stadium, Al Khor	Group stage	Spain	Germany	NaN	NaN	NaN	Luis Enrique	Sergio Busquets	Hansi Flick	Manuel Neuer	NaN
46	2022	Qatar	11/25/2022	Khalifa International Stadium, Doha	Group stage	Netherlands	Ecuador	NaN	NaN	NaN	Louis van Gaal	Virgil van Dijk	Gustavo Alfaro	Enner Valencia	NaN
47	2022	Qatar	11/25/2022	Al Bayt Stadium, Al Khor	Group stage	England	United States	NaN	NaN	NaN	Gareth Southgate	Harry Kane	Gregg Berhalter	Tyler Adams	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
591	1986	Mexico	6/5/1986	Estadio Olï¿½mpico Universitario, Mexico City	Group stage	Korea Republic	Bulgaria	NaN	NaN	NaN	Jung Nam Kim	Park Chang-sun	Ivan Vutsov	Georgi Dimitrov	NaN
593	1986	Mexico	6/4/1986	Estadio Corregidora, Queretaro	Group stage	Uruguay	Germany	NaN	NaN	NaN	Omar Borras	Jorge Barrios	Franz Beckenbauer	Harald Schumacher	NaN
596	1986	Mexico	6/3/1986	Tres de Marzo, Guadalajara	Group stage	Algeria	Northern Ireland	NaN	NaN	NaN	Rabah Saadane	Mahmoud Guendouz	Billy Bingham	Sammy McIlroy	NaN
600	1986	Mexico	6/2/1986	Estadio Universitario, Monterrey	Group stage	Morocco	Poland	NaN	NaN	NaN	Jose Faria	Ezzaki Badou	Antoni Piechniczek	Zbigniew Boniek	NaN
603	1986	Mexico	5/31/1986	Estadio Azteca, Mexico City	Group stage	Bulgaria	Italy	NaN	NaN	NaN	Ivan Vutsov	Georgi Dimitrov	Enzo Bearzot	Gaetano Scirea	NaN

	Manager	Country_managed	Wins
0	Luiz Felipe Scolari	Brazil	16
1	Didier Deschamps	France	14
2	Joachim Löw	Germany	12
3	Carlos Alberto Parreira	[South Africa,Brazil]	11
4	Zlatko Dalić	Croatia	10
5	Franz Beckenbauer	Germany	10
6	Óscar Tabárez	Uruguay	10
7	Carlos Bilardo	Argentina	10
8	Louis van Gaal	Netherlands	9
9	Guus Hiddink	[Australia, Korea Republic, Netherlands]	8
10	Jürgen Klinsmann	[United States,Germany]	7
11	Gareth Southgate	England	7
12	Roberto Martínez	Belgium	7
13	Henri Michel	[CÃ´te d'Ivoire, Morocco, France]	7
14	Aimé Jacquet	France	7
15	Vicente del Bosque	Spain	6
16	Lionel Scaloni	Argentina	6
17	José Pekerman	Colombia	6
18	Alejandro Sabella	Argentina	6
19	Marcello Lippi	Italy	6

Data Analytics project on FIFA World Cup¶

Downloading the Dataset¶

Downloading and loading the dataset¶

Data Cleaning¶

Exploratory Analysis and Visualization¶

Data Manipulation and Visualization¶

Asking and Answering Questions¶

Section 1: Analysis of participant of World Cup from 1986 to 2022¶

Q.1: How many distinct Countries have participated in World Cup from 1986 to 2022?¶

Q.2: World Cup is dominated by which Continents?¶

Bird Eye view of distinct countries and their appearance in World Cup on a World Map¶

Q.3: Which Asian team have participated in World Cup?¶

Q.4: Which African team have participated in World Cup?¶

Section 2: Analysis related to Attendance in World Cup¶

Q1: What is trend of Attendance in the World Cup from 1986 to 2022?¶

Q.2: What is trend of Attendance per match?¶

Q.3: How does trend of Attendance per match in different stages of World Cup looks like?¶

Section 3: Analysis related to goals scored in World Cup¶

Q.1: Is there any significant advantage to goal scoring when a team plays as home team or away team?¶

Q.2: In which year was total goal scored by home team and away team maximum and is there any trend ?¶

Q.3: Is there any significant advantage for home team and away team for penalty goals?¶

Q.4: What is trend of normal goals and penalty goals in World Cup?¶

Q.5: Over the years is there any significant increase in total goals scored?¶

Q.6: Is there any influence on goals scored per match when taking into account different stage/round of World Cup?¶

Section 4: Analysis of Final Stage of World Cup¶

Q.1: Is there any significant effect of digitization on Attendance of World Cup in finals?¶

Q.2: Is there any significant increase in no. of goals scored in finals of World Cup over the years?¶

Q.3: Which team has the most appearance in finals of World Cup from 1986 to 2022?¶

Q.4: Which team has won most World Cup from 1986 to 2022?¶

Section 5: Analysis of triumphs during the World Cup from 1986 to 2022¶

Team with most wins when playing as Home team¶

Team with most wins when playing as Away team¶

Q.1: Which Team has dominated the World Cup by winning the most matches between 1986 and 2022?¶

Q.2: During the world cup campaigns from 1986 to 2022, which manager had the most wins?¶

Q.3: During the world cup campaigns from 1986 to 2022, which player as Captain had most success?¶

Q.4: Which players have won golden boots in World Cup over the years?¶

Inferences and Conclusion¶

Statistics related to World Cup Finals¶

Conclusions related to World Cup teams making history¶

Conclusions related to Attendance in World Cup¶

Most nerve-racking Final¶

Conclusion related to Teams.¶

Most Successful Manager¶

Most Successful player as Captain.¶

References and Future Work¶

Reference¶

Future Work¶

Saving the data¶